Application of serum SERS technology based on thermally annealed silver nanoparticle composite substrate in breast cancer

Cheng, Zhiguang; Li, Hongyi; Chen, Chen; Lv, Xiaoyi; Zuo, Enguang; Han, Shibin; Li, Zhongyuan; Liu, Pei; Li, Hongtao; Chen, Cheng

doi:10.1016/j.pdpdt.2023.103284

Cited by 16 publications

(3 citation statements)

References 40 publications

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“…The experimental findings indicate that the breast cancer detection model based on the enhanced SERS substrate and the machine learning algorithm may be utilized to differentiate breast cancer patients from healthy controls. The aforementioned experimental findings indicate that the SERS technology based on an AgNPs/PSB composite substrate, when paired with machine learning techniques, offers tremendous promise for the speedy and accurate diagnosis of breast cancer patients [ 44 ].…”

Section: The Potential Of Agnps In Nano-oncologymentioning

confidence: 99%

The Role of Silver Nanoparticles in the Diagnosis and Treatment of Cancer: Are There Any Perspectives for the Future?

Takáč

Michalková

Čižmáriková

et al. 2023

Life

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Cancer is a fatal disease with a complex pathophysiology. Lack of specificity and cytotoxicity, as well as the multidrug resistance of traditional cancer chemotherapy, are the most common limitations that often cause treatment failure. Thus, in recent years, significant efforts have concentrated on the development of a modernistic field called nano-oncology, which provides the possibility of using nanoparticles (NPs) with the aim to detect, target, and treat cancer diseases. In comparison with conventional anticancer strategies, NPs provide a targeted approach, preventing undesirable side effects. What is more, nanoparticle-based drug delivery systems have shown good pharmacokinetics and precise targeting, as well as reduced multidrug resistance. It has been documented that, in cancer cells, NPs promote reactive oxygen species (ROS) production, induce cell cycle arrest and apoptosis, activate ER (endoplasmic reticulum) stress, modulate various signaling pathways, etc. Furthermore, their ability to inhibit tumor growth in vivo has also been documented. In this paper, we have reviewed the role of silver NPs (AgNPs) in cancer nanomedicine, discussing numerous mechanisms by which they render anticancer properties under both in vitro and in vivo conditions, as well as their potential in the diagnosis of cancer.

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Section: The Potential Of Agnps In Nano-oncologymentioning

confidence: 99%

The Role of Silver Nanoparticles in the Diagnosis and Treatment of Cancer: Are There Any Perspectives for the Future?

Takáč

Michalková

Čižmáriková

et al. 2023

Life

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“…This finding goes in the opposite direction with respect to the majority of other research works and indicates that the PCA noise reduction and data cleaning capabilities are usually crucial to obtaining the best performances. In fact, several other studies performed the classification through a dimensionality reduction technique and a ML classifier, as in the case of [153]- [155].…”

Section: B Breast Cancermentioning

confidence: 99%

Raman Spectroscopy and AI Applications in Cancer Grading: An Overview

Conforti,

Lazzini,

Russo

et al. 2024

IEEE Access

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Raman spectroscopy (RS) is a label-free molecular vibrational spectroscopy technique that is able to identify the molecular fingerprint of various samples making use of the inelastic scattering of monochromatic light. Because of its advantages of non-destructive and accurate detection, RS is finding more and more use for benign and malignant tissues, tumor differentiation, tumor subtype classification, and section pathology diagnosis, operating either in vivo or in vitro. However, the high specificity of RS comes at a cost. The acquisition rate is low, depth information cannot be directly accessed, and the sampling area is limited. Such limitations can be contained if data pre-and post-processing methods are combined with current methods of Artificial Intelligence (AI), essentially, Machine Learning (ML) and Deep Learning (DL). The latter is modifying the approach to cancer diagnosis currently used to automate many cancer data analyses, and it has emerged as a promising option for improving healthcare accuracy and patient outcomes by abiliting prediction diseases tools. In a very broad context, Artificial Intelligence applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. The application of autonomous methods to datasets generated by RS analysis of benign and malignant tissues could make RS a rapid and stand-alone technique to help pathologists diagnose cancer with very high accuracy. This review describes the current milestones achieved by applying AI-based algorithms to RS analysis, grouped according to seven major types of cancers (Pancreatic, Breast, Skin, Brain, Prostate, Ovarian and Oral cavity). Additionally, it provides a theoretical foundation to tackle both present and forthcoming challenges in this domain. By exploring the current achievements and discussing the relative methodologies, this review offers recapitulative insights on recent and ongoing efforts to position RS as a rapid and effective cancer screening tool for pathologists. Accordingly, we aim to encourage future research endeavors and to facilitate the realization of the full potential of RS and AI applications in cancer grading.

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“…Chi-Square, Singular Vector Decomposition and PCA are used to select the features from the breast cancer dataset 11 . PCA was used to extract the features from the Surface Enhanced Raman spectroscopy (SERS) and Raman Spectroscopy (RS) breast cancer serum 12 . Exploratory Data Analysis (EDA) of the breast cancer dataset was performed using PCA technique 13 .…”

Section: Literature Reviewmentioning

confidence: 99%

An integrative machine learning framework for classifying SEER breast cancer

Manikandan¹,

Durga²,

Ponnuraja

2023

Sci Rep

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Breast cancer is the commonest type of cancer in women worldwide and the leading cause of mortality for females. The aim of this research is to classify the alive and death status of breast cancer patients using the Surveillance, Epidemiology, and End Results dataset. Due to its capacity to handle enormous data sets systematically, machine learning and deep learning has been widely employed in biomedical research to answer diverse classification difficulties. Pre-processing the data enables its visualization and analysis for use in making important decisions. This research presents a feasible machine learning-based approach for categorizing SEER breast cancer dataset. Moreover, a two-step feature selection method based on Variance Threshold and Principal Component Analysis was employed to select the features from the SEER breast cancer dataset. After selecting the features, the classification of the breast cancer dataset is carried out using Supervised and Ensemble learning techniques such as Ada Boosting, XG Boosting, Gradient Boosting, Naive Bayes and Decision Tree. Utilizing the train-test split and k-fold cross-validation approaches, the performance of various machine learning algorithms is examined. The accuracy of Decision Tree for both train-test split and cross validation achieved as 98%. In this study, it is observed that the Decision Tree algorithm outperforms other supervised and ensemble learning approaches for the SEER Breast Cancer dataset.

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Application of serum SERS technology based on thermally annealed silver nanoparticle composite substrate in breast cancer

Cited by 16 publications

References 40 publications

The Role of Silver Nanoparticles in the Diagnosis and Treatment of Cancer: Are There Any Perspectives for the Future?

The Role of Silver Nanoparticles in the Diagnosis and Treatment of Cancer: Are There Any Perspectives for the Future?

Raman Spectroscopy and AI Applications in Cancer Grading: An Overview

An integrative machine learning framework for classifying SEER breast cancer

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